# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
python iresnet.py
"""
from mindspore import nn
import mindspore.ops as ops

__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100']


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """
    3x3 convolution with padding

    # noqa: DAR201
    """
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=3,
                     stride=stride,
                     padding=dilation,
                     pad_mode='pad',
                     group=groups,
                     has_bias=False,
                     dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """
    1x1 convolution

    # noqa: DAR201
    """
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=1,
                     stride=stride,
                     has_bias=False)


class IBasicBlock(nn.Cell):
    '''IBasicBlock
    '''
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 groups=1, base_width=64, dilation=1):
        super(IBasicBlock, self).__init__()
        if groups != 1 or base_width != 64:
            raise ValueError(
                'BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError(
                "Dilation > 1 not supported in BasicBlock")

        self.bn1 = nn.BatchNorm2d(
            inplanes,
            eps=1e-05,
        )
        self.conv1 = conv3x3(inplanes, planes)
        self.bn2 = nn.BatchNorm2d(
            planes,
            eps=1e-05,
        )
        self.prelu = nn.PReLU(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn3 = nn.BatchNorm2d(
            planes,
            eps=1e-05,
        )
        self.downsample = downsample
        self.stride = stride

    def construct(self, x):
        '''construct

        # noqa: DAR201
        '''
        identity = x

        out = self.bn1(x)
        out = self.conv1(out)
        out = self.bn2(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity

        return out


class IResNet(nn.Cell):
    '''IResNet
    '''
    fc_scale = 7 * 7

    def __init__(self,
                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None):
        super(IResNet, self).__init__()
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3,
                               stride=1, padding=1, pad_mode='pad', has_bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
        self.prelu = nn.PReLU(self.inplanes)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
        self.layer2 = self._make_layer(block,
                                       128,
                                       layers[1],
                                       stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block,
                                       256,
                                       layers[2],
                                       stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block,
                                       512,
                                       layers[3],
                                       stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
        self.dropout = nn.Dropout(keep_prob=1.0-dropout)
        self.fc = nn.Dense(512 * block.expansion * self.fc_scale,
                           num_features)
        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
        self.features.gamma.requires_grad = False
        self.reshape = ops.Reshape()
        self.flatten = ops.Flatten()

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        '''make_layer
        '''
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.SequentialCell([
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion, eps=1e-05)
            ])
        layers = []
        layers.append(
            block(self.inplanes, planes, stride, downsample, self.groups,
                  self.base_width, previous_dilation))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(self.inplanes,
                      planes,
                      groups=self.groups,
                      base_width=self.base_width,
                      dilation=self.dilation))

        return nn.SequentialCell(layers)

    def construct(self, x):
        '''construct

        # noqa: DAR201
        '''
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.prelu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.bn2(x)
        x = self.flatten(x)
        # b, c, _, _ = x.shape
        # x = self.reshape(x, (b, -1))
        x = self.dropout(x)
        x = self.fc(x)
        x = self.features(x)

        return x


def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
    model = IResNet(block, layers, **kwargs)
    if pretrained:
        raise ValueError()
    return model


def iresnet18(pretrained=False, progress=True, **kwargs):
    return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
                    progress, **kwargs)


def iresnet34(pretrained=False, progress=True, **kwargs):
    return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
                    progress, **kwargs)


def iresnet50(pretrained=False, progress=True, **kwargs):
    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
                    progress, **kwargs)


def iresnet100(pretrained=False, progress=True, **kwargs):
    return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
                    progress, **kwargs)
